Assurance: Red Score, Happy Users
AI-driven network operations are only as trustworthy as the telemetry beneath them. This lab gives you a live Catalyst Center orchestrating a real fabric, so you learn to read a health score critically, spot the noisy input inflating a false alarm and the missing telemetry hiding a real one.
The problem
Assurance shows a switch's health score dropping into the red and flags a client onboarding issue, but users on that switch report no problems. Meanwhile a genuinely degraded uplink shows a healthy score.
What you'll practice
- Read AI/ML-driven health scores and their KPI breakdown in Assurance
- Distinguish a noisy signal from a real, user-impacting fault
- Verify the telemetry sources feeding a model are complete
- Drive closed-loop remediation via the Intent (REST) API
- Interpret model-driven insight critically, knowing its blind spots
The topology
A live Catalyst Center appliance orchestrates a real fabric and emits the telemetry and assurance data the AI-ops workflows analyse, so health scores are drawn from genuine, and sometimes incomplete, network state.
Topology diagram
Frequently asked
Is the AI just wrong?
Not exactly. A health score is only as good as its inputs: a noisy KPI can inflate a false alarm while a fault with no telemetry stays invisible. The skill is reading the score against the raw data.
What does this have to do with certifications?
It tracks Cisco's AI-operations direction (AgenticOps and the DevNet AI-Infrastructure specialist) rather than a single legacy exam, so it stays current as the platform evolves.
Ready to run this lab yourself?
No setup, no image sourcing. Book a session or ask for a live demo.